



"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase					=       logging.get_logger(__name__)




def 						UpperCAmelCase  (		A						:    List[str]							):

		'''simple docstring'''


		_UpperCAmelCase							=		MobileViTConfig()

		# size of the architecture
		if "mobilevit_s" in mobilevit_name:
				_UpperCAmelCase							=		[144, 192, 240]
				_UpperCAmelCase							=		[16, 32, 64, 96, 128, 160, 640]
		elif "mobilevit_xs" in mobilevit_name:
				_UpperCAmelCase							=		[96, 120, 144]
				_UpperCAmelCase							=		[16, 32, 48, 64, 80, 96, 384]
		elif "mobilevit_xxs" in mobilevit_name:
				_UpperCAmelCase							=		[64, 80, 96]
				_UpperCAmelCase							=		[16, 16, 24, 48, 64, 80, 320]
				_UpperCAmelCase							=		0.05
				_UpperCAmelCase							=		2.0

		if mobilevit_name.startswith('deeplabv3_'							):
				_UpperCAmelCase							=		512
				_UpperCAmelCase							=		16
				_UpperCAmelCase							=		21
				_UpperCAmelCase							=		'pascal-voc-id2label.json'
		else:
				_UpperCAmelCase							=		1000
				_UpperCAmelCase							=		'imagenet-1k-id2label.json'

		_UpperCAmelCase							=		'huggingface/label-files'
		_UpperCAmelCase							=		json.load(open(hf_hub_download(A  ,					A  ,					repo_type='dataset'							)  ,					'r'							)							)
		_UpperCAmelCase							=		{int(A							): v for k, v in idalabel.items()}
		_UpperCAmelCase							=		idalabel
		_UpperCAmelCase							=		{v: k for k, v in idalabel.items()}

		return config




def 						UpperCAmelCase  (		A						:    List[Any]  ,					A						:    Dict=False							):

		'''simple docstring'''


		for i in range(1  ,					6							):
				if f'layer_{i}.' in name:
						_UpperCAmelCase							=		name.replace(f'layer_{i}.'  ,					f'encoder.layer.{i - 1}.'							)

		if "conv_1." in name:
				_UpperCAmelCase							=		name.replace('conv_1.'  ,					'conv_stem.'							)
		if ".block." in name:
				_UpperCAmelCase							=		name.replace('.block.'  ,					'.'							)

		if "exp_1x1" in name:
				_UpperCAmelCase							=		name.replace('exp_1x1'  ,					'expand_1x1'							)
		if "red_1x1" in name:
				_UpperCAmelCase							=		name.replace('red_1x1'  ,					'reduce_1x1'							)
		if ".local_rep.conv_3x3." in name:
				_UpperCAmelCase							=		name.replace('.local_rep.conv_3x3.'  ,					'.conv_kxk.'							)
		if ".local_rep.conv_1x1." in name:
				_UpperCAmelCase							=		name.replace('.local_rep.conv_1x1.'  ,					'.conv_1x1.'							)
		if ".norm." in name:
				_UpperCAmelCase							=		name.replace('.norm.'  ,					'.normalization.'							)
		if ".conv." in name:
				_UpperCAmelCase							=		name.replace('.conv.'  ,					'.convolution.'							)
		if ".conv_proj." in name:
				_UpperCAmelCase							=		name.replace('.conv_proj.'  ,					'.conv_projection.'							)

		for i in range(0  ,					2							):
				for j in range(0  ,					4							):
						if f'.{i}.{j}.' in name:
								_UpperCAmelCase							=		name.replace(f'.{i}.{j}.'  ,					f'.{i}.layer.{j}.'							)

		for i in range(2  ,					6							):
				for j in range(0  ,					4							):
						if f'.{i}.{j}.' in name:
								_UpperCAmelCase							=		name.replace(f'.{i}.{j}.'  ,					f'.{i}.'							)
								if "expand_1x1" in name:
										_UpperCAmelCase							=		name.replace('expand_1x1'  ,					'downsampling_layer.expand_1x1'							)
								if "conv_3x3" in name:
										_UpperCAmelCase							=		name.replace('conv_3x3'  ,					'downsampling_layer.conv_3x3'							)
								if "reduce_1x1" in name:
										_UpperCAmelCase							=		name.replace('reduce_1x1'  ,					'downsampling_layer.reduce_1x1'							)

		for i in range(2  ,					5							):
				if f'.global_rep.{i}.weight' in name:
						_UpperCAmelCase							=		name.replace(f'.global_rep.{i}.weight'  ,					'.layernorm.weight'							)
				if f'.global_rep.{i}.bias' in name:
						_UpperCAmelCase							=		name.replace(f'.global_rep.{i}.bias'  ,					'.layernorm.bias'							)

		if ".global_rep." in name:
				_UpperCAmelCase							=		name.replace('.global_rep.'  ,					'.transformer.'							)
		if ".pre_norm_mha.0." in name:
				_UpperCAmelCase							=		name.replace('.pre_norm_mha.0.'  ,					'.layernorm_before.'							)
		if ".pre_norm_mha.1.out_proj." in name:
				_UpperCAmelCase							=		name.replace('.pre_norm_mha.1.out_proj.'  ,					'.attention.output.dense.'							)
		if ".pre_norm_ffn.0." in name:
				_UpperCAmelCase							=		name.replace('.pre_norm_ffn.0.'  ,					'.layernorm_after.'							)
		if ".pre_norm_ffn.1." in name:
				_UpperCAmelCase							=		name.replace('.pre_norm_ffn.1.'  ,					'.intermediate.dense.'							)
		if ".pre_norm_ffn.4." in name:
				_UpperCAmelCase							=		name.replace('.pre_norm_ffn.4.'  ,					'.output.dense.'							)
		if ".transformer." in name:
				_UpperCAmelCase							=		name.replace('.transformer.'  ,					'.transformer.layer.'							)

		if ".aspp_layer." in name:
				_UpperCAmelCase							=		name.replace('.aspp_layer.'  ,					'.'							)
		if ".aspp_pool." in name:
				_UpperCAmelCase							=		name.replace('.aspp_pool.'  ,					'.'							)
		if "seg_head." in name:
				_UpperCAmelCase							=		name.replace('seg_head.'  ,					'segmentation_head.'							)
		if "segmentation_head.classifier.classifier." in name:
				_UpperCAmelCase							=		name.replace('segmentation_head.classifier.classifier.'  ,					'segmentation_head.classifier.'							)

		if "classifier.fc." in name:
				_UpperCAmelCase							=		name.replace('classifier.fc.'  ,					'classifier.'							)
		elif (not base_model) and ("segmentation_head." not in name):
				_UpperCAmelCase							=		'mobilevit.' + name

		return name




def 						UpperCAmelCase  (		A						:    Optional[int]  ,					A						:    Union[str, Any]  ,					A						:    Tuple=False							):

		'''simple docstring'''


		if base_model:
				_UpperCAmelCase							=		''
		else:
				_UpperCAmelCase							=		'mobilevit.'

		for key in orig_state_dict.copy().keys():
				_UpperCAmelCase							=		orig_state_dict.pop(A							)

				if key[:8] == "encoder.":
						_UpperCAmelCase							=		key[8:]

				if "qkv" in key:
						_UpperCAmelCase							=		key.split('.'							)
						_UpperCAmelCase							=		int(key_split[0][6:]							) - 1
						_UpperCAmelCase							=		int(key_split[3]							)
						_UpperCAmelCase							=		model.get_submodule(f'{model_prefix}encoder.layer.{layer_num}'							)
						_UpperCAmelCase							=		layer.transformer.layer[transformer_num].attention.attention.all_head_size
						_UpperCAmelCase							=		(
						    f'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
						)
						if "weight" in key:
								_UpperCAmelCase							=		val[:dim, :]
								_UpperCAmelCase							=		val[dim : dim * 2, :]
								_UpperCAmelCase							=		val[-dim:, :]
						else:
								_UpperCAmelCase							=		val[:dim]
								_UpperCAmelCase							=		val[dim : dim * 2]
								_UpperCAmelCase							=		val[-dim:]
				else:
						_UpperCAmelCase							=		val

		return orig_state_dict




def 						UpperCAmelCase  (		):

		'''simple docstring'''


		_UpperCAmelCase							=		'http://images.cocodataset.org/val2017/000000039769.jpg'
		_UpperCAmelCase							=		Image.open(requests.get(A  ,					stream=A							).raw							)
		return im




@torch.no_grad()
def 						UpperCAmelCase  (		A						:    Union[str, Any]  ,					A						:    Dict  ,					A						:    Any  ,					A						:    Union[str, Any]=False							):

		'''simple docstring'''


		_UpperCAmelCase							=		get_mobilevit_config(A							)

		# load original state_dict
		_UpperCAmelCase							=		torch.load(A  ,					map_location='cpu'							)

		# load 🤗 model
		if mobilevit_name.startswith('deeplabv3_'							):
				_UpperCAmelCase							=		MobileViTForSemanticSegmentation(A							).eval()
		else:
				_UpperCAmelCase							=		MobileViTForImageClassification(A							).eval()

		_UpperCAmelCase							=		convert_state_dict(A  ,					A							)
		model.load_state_dict(A							)

		# Check outputs on an image, prepared by MobileViTImageProcessor
		_UpperCAmelCase							=		MobileViTImageProcessor(crop_size=config.image_size  ,					size=config.image_size + 32							)
		_UpperCAmelCase							=		image_processor(images=prepare_img()  ,					return_tensors='pt'							)
		_UpperCAmelCase							=		model(**A							)
		_UpperCAmelCase							=		outputs.logits

		if mobilevit_name.startswith('deeplabv3_'							):
				assert logits.shape == (1, 21, 32, 32)

				if mobilevit_name == "deeplabv3_mobilevit_s":
						_UpperCAmelCase							=		torch.tensor(
						    [
						        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
						        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
						        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
						    ]							)
				elif mobilevit_name == "deeplabv3_mobilevit_xs":
						_UpperCAmelCase							=		torch.tensor(
						    [
						        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
						        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
						        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
						    ]							)
				elif mobilevit_name == "deeplabv3_mobilevit_xxs":
						_UpperCAmelCase							=		torch.tensor(
						    [
						        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
						        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
						        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
						    ]							)
				else:
						raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}'							)

				assert torch.allclose(logits[0, :3, :3, :3]  ,					A  ,					atol=1e-4							)
		else:
				assert logits.shape == (1, 1000)

				if mobilevit_name == "mobilevit_s":
						_UpperCAmelCase							=		torch.tensor([-0.9866, 0.2392, -1.1241]							)
				elif mobilevit_name == "mobilevit_xs":
						_UpperCAmelCase							=		torch.tensor([-2.4761, -0.9399, -1.9587]							)
				elif mobilevit_name == "mobilevit_xxs":
						_UpperCAmelCase							=		torch.tensor([-1.9364, -1.2327, -0.4653]							)
				else:
						raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}'							)

				assert torch.allclose(logits[0, :3]  ,					A  ,					atol=1e-4							)

		Path(A							).mkdir(exist_ok=A							)
		print(f'Saving model {mobilevit_name} to {pytorch_dump_folder_path}'							)
		model.save_pretrained(A							)
		print(f'Saving image processor to {pytorch_dump_folder_path}'							)
		image_processor.save_pretrained(A							)

		if push_to_hub:
				_UpperCAmelCase							=		{
				    'mobilevit_s': 'mobilevit-small',
				    'mobilevit_xs': 'mobilevit-x-small',
				    'mobilevit_xxs': 'mobilevit-xx-small',
				    'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
				    'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
				    'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
				}

				print('Pushing to the hub...'							)
				_UpperCAmelCase							=		model_mapping[mobilevit_name]
				image_processor.push_to_hub(A  ,					organization='apple'							)
				model.push_to_hub(A  ,					organization='apple'							)


if __name__ == "__main__":
					lowercase					=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--mobilevit_name''',
					    default='''mobilevit_s''',
					    type=str,
					    help=(
					        '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
					        ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
					    ),
					)
					parser.add_argument(
					    '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
					)

					lowercase					=       parser.parse_args()
					convert_movilevit_checkpoint(
					    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)



import datasets


__a    :	Optional[Any]         =    """\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__a    :	Optional[Any]         =    """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__a    :	str         =    """
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				return (preds == labels).mean()




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION					)
class   _UpperCamelCase (		datasets.Metric					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							        }		) ,  codebase_urls=[] ,  reference_urls=[] ,  format='''numpy''' ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							return {"accuracy": simple_accuracy(lowerCAmelCase__ ,  lowerCAmelCase__		)}